🎬 VideoDeepFakeDetection uses AI to authenticate videos through a multi-step process, identifying potential deepfakes for enhanced content reliability.

Detectify is a deep learning system that detects AI-generated fake videos (deepfakes) using CNN and LSTM-based RNNs. Trained on datasets like Face-Forensic++, Deepfake Detection Challenge, and Celeb-DF, Detectify offers real-time video manipulation detection to combat misinformation and misuse of deepfake technology.

Audio Deepfake Detection is a web app that utilizes machine learning techniques to analyze audio files and determine if they are real or generated by deepfake algorithms. It features audio file upload, audio feature extraction, comparison with a pre-defined dataset, and classification of audio as real or deepfake.

Chrome Extension for Real-Time Deepfake Detection with AI

This website is a deepfake detection platform where users can upload videos and get results with confidence percentages. Built with Django and trained on Celeb-DF, DFD, and FaceForensics++ datasets, it uses ResNeXt CNN and LSTM models for accurate real-time detection. A research paper on this project is also published (PDF).

Real-time Deepfake Video Detection System using ResNeXt + LSTM | Django + PyTorch

This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features

A deep learning-based web application for deepfake video detection, powered by the fine-tuned XceptionNet (Extreme Inception) model. The system allows users to upload videos for deepfake detection, processes them through the trained model, and provides results via a clean Django-based web interface.

AI-powered OSINT platform for image authenticity verification, metadata extraction, and deepfake detection.

🎥 CyberEye is an AI-powered Flask web app for detecting deepfakes in videos by analyzing facial landmarks and consistency. Upload videos, review frame-by-frame analysis, and get visual classification results with an intuitive interface.

This repository contains the code and resources for a deepfake detection system. The system uses a neural network trained on a dataset generated from Metalabs videos and provides a web interface for users to interact with the model. This project won 3rd prize in the SRMIST Ideathon 2024 held at SRM University, Kattankulathur, Chennai.

Deepfake detection simple by image

AI-powered platform to detect deepfake images and videos using advanced deep learning models. Ensures content authenticity, combats misinformation, and protects user identity — all through a sleek, secure, and modern web interface.

A Flask-based web application for detecting deepfake videos using a ResNeXt50+LSTM model. Users can upload videos, and the model analyzes frames to classify them as real or fake. The app supports CPU-based inference and includes face detection for better accuracy.